Macroblock Classification Method for Video Applications Involving Motions
This work addresses video processing challenges for applications involving motions, but it appears incremental as it builds on existing motion vector analysis techniques.
The paper tackles video processing by classifying macroblocks based on motion vector analysis to describe frame content, demonstrating that this low-complexity method effectively captures frame characteristics and enables applications like shot change detection and motion discontinuity detection.
In this paper, a macroblock classification method is proposed for various video processing applications involving motions. Based on the analysis of the Motion Vector field in the compressed video, we propose to classify Macroblocks of each video frame into different classes and use this class information to describe the frame content. We demonstrate that this low-computation-complexity method can efficiently catch the characteristics of the frame. Based on the proposed macroblock classification, we further propose algorithms for different video processing applications, including shot change detection, motion discontinuity detection, and outlier rejection for global motion estimation. Experimental results demonstrate that the methods based on the proposed approach can work effectively on these applications.